SoroushVahidi/scidocs huggingface dataset:
Towards Frequent Subgraph Mining on Single Large Uncertain Graphs
(2015)

Uncertainty is intrinsic to a wide spectrum of real-life applications, which inevitably applies to graph data. Representative uncertain graphs are seen in bio-informatics, social networks, etc. This paper motivates the problem of frequent subgraph mining on single uncertain graphs. We present an enumeration-evaluation algorithm to solve the problem. By showing support computation on an uncertain graph is #P-hard, we develop an approximation algorithm with accuracy guarantee for this purpose. To enhance the solution, we devise optimization techniques to achieve better mining performance. Experiment results on real-life data confirm the usability of the algorithm.
Use of RFID Based Real Time Location Tracking System to Curb Diversion of Transit Goods in East Africa
(2009)

In this paper, RFID based Real Time Location Tracking System (RTLS) for tracking transit goods is discussed. The system was designed to address the problem of diversions of transit wet cargo destined for hinterland countries into the local markets, so as to evade payment of duties and taxes. Transit goods are currently tracked manually and by use of manual physical escorts. Reliance on manual methods has rendered the system open to abuse by unscrupulous traders and clearing agents; at times in collusion with government officers. To be effective in tracking wet cargo, the required solution was established to be one that not only provides information in real time but also with features for intervention when cargo is perceived to be at risk. This paper discusses the design and implementation of RTLS for tracking wet cargo, and points out the strenghts, benefits, weaknesses and limitations of the solution. The effectiveness of the solution was demonstrated in addressing the weaknesses of the current manual tracking system in curbing diversion for wet cargo. In addition, the use of RTLS led to a reduction in turn around times by between thirty to forty percent compared to those which are manualy tracked. KeywordsElectronic Seals, RFID, Transit goods, Tanker Tracking, Real Time Location Tracking Systems, Cargo Tracking
Review of Face Recognition Techniques
(2015)

Use of Biometrics and widespread acceptability for person authentication has instigated several techniques. Now a day’s security is very important. Biometrics has received a great attention. Face biometrics, is useful person’s authentication that recognizes face. In this paper first we represent an overview of face recognition and discuss the methodology and functioning. Thereafter we represent face recognition techniques which are recently used including their advantages and disadvantages. A large number of face recognition algorithms have been developed. In this paper an attempt is made to review a wide range of methods including PCA, Eigen face, LDA for recognition and hybrid combination if this techniques. Under the various illumination and expression condition of face images some techniques specified here also improves the efficiency of face recognition. Keywords— Face Recognition, Principal Component Analysis, Eigen Face, Linear Discriminant Analysis
Experiences of System-Level Model-Based GUI Testing of an Android Application
(2011)

This paper presents experiences in model-based graphical user interface testing of Android applications. We present how model-based testing and test automation was implemented with Android, including how applications were modeled, how tests were designed and executed, and what kind of problems were found in the tested application during the whole process. The main focus is on a case study that was performed with an Android application, the BBC News Widget. Our goal is to present actual data on the experiences and to discuss if advantages can be gained using model-based testing when compared with traditional graphical user interface testing. Another contribution of this paper is a description of a keyword-based test automation tool that was implemented for the Android emulator during the case study. All the models and the tools created or used in this case study are available as open source.
Network Slicing in 5G Mobile Communication Architecture, Profit Modeling, and Challenges
(2017)

Efficient flexibility and higher system scalability call for enhanced network performance, better energy consumption, lower infrastructure cost, and effective resource utilization. To accomplish this, an architectural optimization and reconstruction of existing cellular network is required. Network slicing is considered to be one of the key enablers and an architectural answer of communication system of 2020 and beyond. Traditional mobile operators provide all types of services to various kinds of customers through a single network, however, with the deployment of network slicing operators are now able to divide entire network into different slices each with its own configuration and specific Quality of Service (QoS) requirements. In a slice-based network, each slice will be considered as a separate logical network. In this way, the infrastructure utilization and resource allocation will be much more energy and cost efficient in comparison to traditional network. In this paper, we provided a comprehensive discussion on concept and system architecture of network slicing with particular focus on its business aspect and profit modeling. We throughly discussed two different dimensions of profit modeling, so called Own-Slice Implementation and Resource Leasing for Outsourced Slices. We further addressed open research directions and existing challenges with the purpose of motivating new advances and adding realistic solutions to this emerging technology.
A 0.8V, 560fJ/bit, 14Gb/s injection-locked receiver with input duty-cycle distortion tolerable edge-rotating 5/4X sub-rate CDR in 65nm CMOS
(2014)

A quarter-rate forwarded-clock receiver utilizes an edge-rotating 5/4X sub-rate CDR for improved jitter tolerance with low power overhead relative to conventional 2X oversampling CDR systems. Low-voltage operation is achieved with efficient quarter-rate clock generation from an injection-locked oscillator (ILO) and through automatic independent phase rotator control that optimizes timing margin of each input quantizer in the presence of receive-side clock static phase errors and transmitter duty-cycle distortion (DCD). Fabricated in GP 65nm CMOS, the receiver operates up to 16Gb/s with a BER<;10-12, achieves a 1MHz phase tracking bandwidth, tolerates ±50%UIpp DCD on input data, and has 14Gb/s energy efficiency of 560fJ/bit at VDD=0.8V.
Sentiment Analysis of Citations Using Word2vec
(2017)

Citation sentiment analysis is an important task in scientific paper analysis. Existing machine learning techniques for citation sentiment analysis are focusing on labor-intensive feature engineering, which requires large annotated corpus. As an automatic feature extraction tool, word2vec has been successfully applied to sentiment analysis of short texts. In this work, I conducted empirical research with the question: how well does word2vec work on the sentiment analysis of citations? The proposed method constructed sentence vectors (sent2vec) by averaging the word embeddings, which were learned from Anthology Collections (ACL-Embeddings). I also investigated polarity-specific word embeddings (PS-Embeddings) for classifying positive and negative citations. The sentence vectors formed a feature space, to which the examined citation sentence was mapped to. Those features were input into classifiers (support vector machines) for supervised classification. Using 10-cross-validation scheme, evaluation was conducted on a set of annotated citations. The results showed that word embeddings are effective on classifying positive and negative citations. However, hand-crafted features performed better for the overall classification.
Design, simulation and implementation of a self-oscillating control circuit to drive series resonant inverter feeding a brazing induction furnace
(2016)

This research deals with the design and simulation of induction furnace power source (inverter) using MATLAB package. This source designed to lock on the resonant frequency of the load by using self-oscillating technique, also it has the capability to control the power supplied to the load using phase shift pulse width modulation (PSPWM) technique. These characteristics used to overcome the load nonlinear behavior during the brazing process and to achieve soft switching of the inverter elements. Also, the inverter has the capability to operate with or without load (workpiece). The implemented prototype operates at a frequency range (50-100)kHz and 10kW was successfully used for brazing two copper workpieces.
A complete solution to the inverse kinematics problem for 4-DOF manipulator robot
(2013)

A complete closed-form solution to the inverse kinematics problem for a 4-DOF manipulator robot is proposed. With the discussion of the existence of the offset and the distance between the axis of 1st joint and 2nd joint, all the possible solutions and singular configurations are presented. “Task attitude” is defined to describe the orientation of the end effector, which is convenient to solve the inverse kinematics problems in engineering. Finally, simulation of path-planning is performed, and the efficiency of the proposed method is verified through Mathematica.
Stochastic Gradient Hamiltonian Monte Carlo
(2014)

Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals. The popularity of such methods has grown significantly in recent years. However, a limitation of HMC methods is the required gradient computation for simulation of the Hamiltonian dynamical system—such computation is infeasible in problems involving a large sample size or streaming data. Instead, we must rely on a noisy gradient estimate computed from a subset of the data. In this paper, we explore the properties of such a stochastic gradient HMC approach. Surprisingly, the natural implementation of the stochastic approximation can be arbitrarily bad. To address this problem we introduce a variant that uses second-order Langevin dynamics with a friction term that counteracts the effects of the noisy gradient, maintaining the desired target distribution as the invariant distribution. Results on simulated data validate our theory. We also provide an application of our methods to a classification task using neural networks and to online Bayesian matrix factorization.
Bayesian Learning via Stochastic Gradient Langevin Dynamics
(2011)

In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm we show that the iterates will converge to samples from the true posterior distribution as we anneal the stepsize. This seamless transition between optimization and Bayesian posterior sampling provides an inbuilt protection against overfitting. We also propose a practical method for Monte Carlo estimates of posterior statistics which monitors a “sampling threshold” and collects samples after it has been surpassed. We apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients.
Performance-oriented DevOps: A Research Agenda
(2015)

DevOps is a trend towards a tighter integration between development (Dev) and operations (Ops) teams. The need for such an integration is driven by the requirement to continuously adapt enterprise applications (EAs) to changes in the business environment. As of today, DevOps concepts have been primarily introduced to ensure a constant flow of features and bug fixes into new releases from a functional perspective. In order to integrate a non-functional perspective into these DevOps concepts this report focuses on tools, activities, and processes to ensure one of the most important quality attributes of a software system, namely performance. Performance describes system properties concerning its timeliness and use of resources. Common metrics are response time, throughput, and resource utilization. Performance goals for EAs are typically defined by setting upper and/or lower bounds for these metrics and specific business transactions. In order to ensure that such performance goals can be met, several activities are required during development and operation of these systems as well as during the transition from Dev to Ops. Activities during development are typically summarized by the term Software Performance Engineering (SPE), whereas activities during operations are called Application Performance Management (APM). SPE and APM were historically tackled independently from each other, but the newly emerging DevOps concepts require and enable a tighter integration between both activity streams. This report presents existing solutions to support this integration as well as open research challenges in this area. The report starts by defining EAs and summarizes their characteristics that make performance evaluations for these systems particularly challenging. It continues by describing our understanding of DevOps and explaining the roots of this trend to set the context for the remaining parts of the report. Afterwards, performance management activities that are common in both life cycle phases are explained, until the particularities of SPE and APM are discussed in separate sections. Finally, the report concludes by outlining activities and challenges to support the rapid iteration between Dev and Ops.
Programs, life cycles, and laws of software evolution
(1980)

By classifying programs according to their relationship to the environment in which they are executed, the paper identifies the sources of evolutionary pressure on computer applications and programs and shows why this results in a process of never ending maintenance activity. The resultant life cycle processes are then briefly discussed. The paper then introduces laws of Program Evolution that have been formulated following quantitative studies of the evolution of a number of different systems. Finally an example is provided of the application of Evolution Dynamics models to program release planning.
BFC: High-performance distributed big-file cloud storage based on key-value store
(2015)

Nowadays, cloud-based storage services are rapidly growing and becoming an emerging trend in data storage field. There are many problems when designing an efficient storage engine for cloud-based systems with some requirements such as big-file processing, lightweight meta-data, low latency, parallel I/O, deduplication, distributed, high scalability. Key-value stores played an important role and showed many advantages when solving those problems. This paper presents about Big File Cloud (BFC) with its algorithms and architecture to handle most of problems in a big-file cloud storage system based on key-value store. It is done by proposing low-complicated, fixed-size meta-data design, which supports fast and highly-concurrent, distributed file I/O, several algorithms for resumable upload, download and simple data deduplication method for static data. This research applied the advantages of ZDB - an in-house key-value store which was optimized with auto-increment integer keys for solving big-file storage problems efficiently. The results can be used for building scalable distributed data cloud storage that support big-file with size up to several terabytes.
A Meta-plugin for Bespoke Data Management in WordPress
(2012)

WordPress is a powerful and extensible platform for webbased information publishing and management. While the WordPress core is targeted to the publication of chronologically ordered textual articles typical of blogs, users have developed plugins as well as themes to support the data management requirements of specific domains such as e-commerce or e-learning. However, the creation of such plugins requires development skills and effort. We present a meta-plugin that automatically generates bespoke plugins for data management based on user-defined ER models. We illustrate the approach using an example of creating a WordPress site for managing information about courses.
Dendrites, deep learning, and sequences in the hippocampus.
(2017)

The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label "neural." These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation.
Multivariate Network Exploration and Presentation: From Detail to Overview via Selections and Aggregations
(2014)

Network data is ubiquitous; e-mail traffic between persons, telecommunication, transport and financial networks are some examples. Often these networks are large and multivariate, besides the topological structure of the network, multivariate data on the nodes and links is available. Currently, exploration and analysis methods are focused on a single aspect; the network topology or the multivariate data. In addition, tools and techniques are highly domain specific and require expert knowledge. We focus on the non-expert user and propose a novel solution for multivariate network exploration and analysis that tightly couples structural and multivariate analysis. In short, we go from Detail to Overview via Selections and Aggregations (DOSA): users are enabled to gain insights through the creation of selections of interest (manually or automatically), and producing high-level, infographic-style overviews simultaneously. Finally, we present example explorations on real-world datasets that demonstrate the effectiveness of our method for the exploration and understanding of multivariate networks where presentation of findings comes for free.
Evaluating and Developing Theories in the Information Systems Discipline
(2012)

This paper articulates a framework and criteria that can be used to evaluate the quality of theories. While the framework and criteria have general applicability, my focus is the evaluation of theories within the information systems discipline. To illustrate the usefulness of the framework and criteria, I show how they can be employed to pinpoint the strengths and weaknesses of a theory which, based upon citation evidence, has had a significant impact on other researchers within the information systems discipline. Because the evaluation of existing theories often provides the basis for refining existing theories or building new theories, I also show how the framework and criteria can be used to inform the development of high-quality theory.
Boosting Algorithms for Parallel and Distributed Learning
(2002)

The growing amount of available information and its distributed and heterogeneous nature has a major impact on the field of data mining. In this paper, we propose a framework for parallel and distributed boosting algorithms intended for efficient integrating specialized classifiers learned over very large, distributed and possibly heterogeneous databases that cannot fit into main computer memory. Boosting is a popular technique for constructing highly accurate classifier ensembles, where the classifiers are trained serially, with the weights on the training instances adaptively set according to the performance of previous classifiers. Our parallel boosting algorithm is designed for tightly coupled shared memory systems with a small number of processors, with an objective of achieving the maximal prediction accuracy in fewer iterations than boosting on a single processor. After all processors learn classifiers in parallel at each boosting round, they are combined according to the confidence of their prediction. Our distributed boosting algorithm is proposed primarily for learning from several disjoint data sites when the data cannot be merged together, although it can also be used for parallel learning where a massive data set is partitioned into several disjoint subsets for a more efficient analysis. At each boosting round, the proposed method combines classifiers from all sites and creates a classifier ensemble on each site. The final classifier is constructed as an ensemble of all classifier ensembles built on disjoint data sets. The new proposed methods applied to several data sets have shown that parallel boosting can achieve the same or even better prediction accuracy considerably faster than the standard sequential boosting. Results from the experiments also indicate that distributed boosting has comparable or slightly improved classification accuracy over standard boosting, while requiring much less memory and computational time since it uses smaller data sets.
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
(2018)

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanism that used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity and availability of the services. The speed of the IDS is very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The techniques J48, Random Forest, Random Tree, MLP, Naïve Bayes and Bayes Network classifiers have been chosen for this study. It has been proven that the Random forest classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type (DOS, R2L, U2R, and PROBE). Keywords— IDS, DDoS, MLP, Naïve Bayes, Random Forest
Automated coverage-driven testing: combining symbolic execution and model checking
(2016)

软件测试是工业界最常用的软件验证技术, 而白盒测试是最基础的软件测试方法。 为了提高白盒测试有效性, 本文创新性地提出了一种混合的覆盖驱动测试方法, 它接受一个待测程序和目标覆盖准则为输入, 然后为可达的测试对象自动生成测试用例, 同时也检测出不可达的测试对象。 本文将驱动测试问题转化为特定路径的寻找问题, 提出了带引导的符号执行技术和增强的模型检查技术, 最终又将两项技术结合在一起, 进一步提高测试覆盖率并降低测试时间, 实现优势互补。